Abstract:
The Cleanstrain code will simultaneously estimate the tidal constituents, pressure admittance, offsets, rate changes, and other terms using least-squares but, importantly, incorporating the temporally correlated nature of strain data (that is, the power spectra is red). I am aware of two alternative methods to analyze time-series data from strainmeters; one being baytap, and the other being the ... application of Kalman filter techniques. Although Kalman filter techniques are available in the literature, I'm not aware of any written as an easy application to strainmeter data. Although baytap is rigorous in its approach (ie, it assumes that the strainmeter data have a background noise process that is a combination of random walk and white noise), one needs to pre-clean the data by estimating and removing offsets and other transients and removing spurious observations which all might bias the results from baytap. Much subjectivity is used to pre-clean the data; the subjectivity is minimized using cleanstrain+.

This code will not automatically fix outliers and offsets. But, it will help identify those problems in the data and, once identified (with a time), it will then fix these problems. The cleanstrain package also includes a script called glitch+ which will also help identify the times of problems in the data.